Be All You Can Be or Longing to Be: Racialized soldiers, the Canadian military experience and the Im/Possibility of belonging to the nation
Bibliographic record
Abstract
Military conquest and intervention have played a central role in the making and maintaining of empires for centuries. The question of who serves in those militaries remains extremely significant in settler societies, particularly in the contemporary negotiation of citizenship, immigration, national belonging and identity. In this dissertation I ask the following questions: Who is the racialized soldier subject? How do racialized soldiers negotiate national belonging? What are their experiences in the Canadian military? This research study examines the racial underpinnings of citizenship through an exploration of the experiences of racialized soldier subjects in the Canadian Forces in the post 9/11 era. The study is based on in-depth interviews with twenty-five reserve and retired soldiers, both men and women from various racialized communities in Canada. Drawing on a â race cognizantâ poststructuralism and feminist critical race theories of subjectivity, masculinity, nation formation, citizenship and racial discourse, this research reveals several important findings. First, this dissertation reveals that while the Canadian military projects itself as a racially diverse institution, the reality is quite different. In this thesis, I theorize military recruitment as a project of inclusion into a white culture. Racialized soldiers join the military family for a number of reasons, but often experience the military as duplicitous and are at times met with enormous bureaucracy. Their journeys into the military reveal complex structural conditions that make joining the military a viable option. Once in the military, interviews with racialized soldiers reveal the visceral nature of whiteness within the Canadian military apparatus. The narratives of soldiers of colour also show that despite the amount of racial labour involved in negotiating this white space, there still remains a desire to belong. In battle zones, soldiers of colour often display an ambivalence if not a critique of the racist and Orientalist underpinnings of war. Yet they remain dedicated and are proud of their military service. These findings have important implications for understanding institutional diversity, the experiences of military life and finally, the undeniable role of racism in modern day warfare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.059 | 0.026 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".